EDA for part 3 alternative fuel charging station in the US

Part 3: Alternative Fuel Charging Station

As stated above in the previous parts, as the EV sales and registration have been increasing in the past decades, some may wonder: is the complimentory facilities keeping up with the boosting of EV? So let us take a closer look at the charging/alternative fuel station’s distribution in the US. The data is retrieved from U.S. Department of Energy

Code

import pandas as pd
import numpy as np

df = pd.read_csv("data/alt_fuel_stations (Mar 15 2024).csv")

/var/folders/fd/glpyb_f16c52ys5wz5cgrb5r0000gn/T/ipykernel_88279/2398719105.py:4: DtypeWarning:

Columns (6,16,20,31,33,39,40,41,43,46,52,55,57,58,60,62,65,67,69,71) have mixed types. Specify dtype option on import or set low_memory=False.

Within this dataset, there are 6 alternative fuel kinds in total, each of them reprensents:

ELEC: Electricity.

E85: Ethanol Fuel Blend (85% ethanol, 15% gasoline).

LPG: Liquefied Petroleum Gas.

BD: Biodiesel.

CNG: Compressed Natural Gas. RD: Renewable Diesel.

LNG: Liquefied Natural Gas.

HY: Hydrogen.

Code

df['Fuel Type Code'].value_counts()

Fuel Type Code
ELEC    84148
E85      4529
LPG      3521
BD       1702
CNG      1513
RD        611
LNG       125
HY        125
Name: count, dtype: int64

Histogram with drop-down bar for each state.

Code

import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
import pandas as pd

states = df['State'].unique()

# Create a figure
fig = go.Figure()

# Add one trace (bar) for each state to the figure
for state in states:
    fig.add_trace(
        go.Histogram(
            x=df[df['State'] == state]['Fuel Type Code'],
            name=state,  # This will be the label in the dropdown
            visible=(state == states[0])  # Only the first state is visible initially
        )
    )

# Create dropdown menus
dropdown_buttons = [
    {'label': state,
     'method': 'update',
     'args': [{'visible': [s == state for s in states]},
              {'title': f'Histogram of Values for {state}'}]}  # This changes the figure title
    for state in states]

# Add dropdown to the figure
fig.update_layout(
    updatemenus=[{'buttons': dropdown_buttons,
                  'direction': 'down',
                  'showactive': True,}]
)

# Show the figure
iplot(fig)

Horizontal Stacked histogram for fuel type for each state.

Code

states = df['State'].unique()
fuel_types = df['Fuel Type Code'].unique()

# Initialize an empty list for the traces
traces = []

# Create a trace for each fuel type
for fuel_type in fuel_types:
    # Initialize a list to hold the counts for each state
    counts = []
    for state in states:
        # Count the occurrences of the fuel type in the current state
        count = df[(df['State'] == state) & (df['Fuel Type Code'] == fuel_type)].shape[0]
        counts.append(count)
    
    # Create a horizontal bar trace for the current fuel type
    traces.append(go.Bar(
        y=states,  # Swap x and y
        x=counts,  # Swap x and y
        name=fuel_type,
        orientation='h'  # Specify horizontal orientation
    ))

# Create the figure and update layout for stacked histogram (bar chart)
fig = go.Figure(data=traces)
fig.update_layout(
    barmode='stack',
    title_text='Stacked Histogram of Fuel Types by State',  # Chart title
    yaxis_title_text='State',  
    xaxis_title_text='Frequency', 
    height = 1000,
    width = 800
)

# Show the plot
fig.show()

From the horizontal stacked histogram categorized by states, we can see that for each state: the majority of alternative fuel stations are electrical charging station, which the proportion is rather donimant compares to other alternative kinds. Specifically, the number of alternative fuel stations (both electric and all combined) in California (CA) out-numbered other states. A more straight-forward geographical demonstration below displayes the density of the stations on a US, and a world map.

Geospatial Dataset:

Code

import geopandas as gpd
import pandas as pd
import plotly.express as px

geojson_path = 'data/alt_fuel_stations (Mar 15 2024).geojson'
gdf = gpd.read_file(geojson_path)

state_col_name = 'state'  
fuel_type_col_name = 'fuel_type_code'  

As scattered points

Code

df = pd.DataFrame(gdf.drop(columns='geometry'))
df['longitude'] = gdf.geometry.x
df['latitude'] = gdf.geometry.y

fuel_type_col = 'fuel_type_code'

fig = px.scatter_geo(df,
                     lon='longitude',
                     lat='latitude',
                     color=fuel_type_col, 
                     hover_name=fuel_type_col, 
                     title='Fuel Stations by Fuel Type across the USA')

fig.update_traces(marker=dict(size=3))

fig.update_layout(
    height = 500,
    width = 800
)

fig.update_geos(
    projection_type="kavrayskiy7",  
    landcolor="lightgrey",
    lakecolor="white",
    showocean=True, oceancolor="azure",
)

fig.show()

As heat map

Code

import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
import pandas as pd

geojson_path = 'data/alt_fuel_stations (Mar 15 2024).geojson'
df = pd.DataFrame(gdf.drop(columns='geometry'))
df['longitude'] = gdf.geometry.x
df['latitude'] = gdf.geometry.y
df_aggregated = df.groupby(['fuel_type_code', 'latitude', 'longitude']).size().reset_index(name='Count')

fuel_types = df_aggregated['fuel_type_code'].unique()

# Create a figure
fig = go.Figure()

for fuel_type in fuel_types:
    df_filtered = df_aggregated[df_aggregated['fuel_type_code'] == fuel_type]
    
    # Create a heatmap for each fuel type
    fig.add_trace(
        go.Densitymapbox(lat=df_filtered['latitude'], lon=df_filtered['longitude'],
                         z=df_filtered['Count'], name=fuel_type, visible=False)
    )

# Make the first fuel type visible by default
fig.data[0].visible = True

# Set mapbox style
fig.update_layout(mapbox_style="light", mapbox_center_lon=180)
fig.update_layout(mapbox=dict(center=dict(lat=38, lon=-94), zoom=1))

# Create dropdown buttons
buttons = []

for i, fuel_type in enumerate(fuel_types):
    button = dict(
        label=fuel_type,
        method="update",
        args=[{"visible": [False] * len(fuel_types)},
              {"title": f"Heatmap of {fuel_type} Fuel Stations"}])
    button["args"][0]["visible"][i] = True  
    buttons.append(button)

fig.update_layout(
    height = 500,
    width = 800,
    updatemenus=[
        dict(
            buttons=buttons,
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

mapbox_access_token = 'pk.eyJ1IjoiY2F0aHl6d24iLCJhIjoiY2x1a2VnN2FjMDNhdzJpbGw5Y3Jud2Q5MyJ9.bjGv9ATrYWnL4B66WwCH7g'
fig.update_layout(mapbox_accesstoken=mapbox_access_token)

fig.show()

From the geographical vizualization: heat map for each type of alternative fuel station, we could see that more stations are gathered in california, especially electric charging stations. According to one journal that: established in January 2018, the California Zero-Emission Vehicle (ZEV) Action Plan set an ambitious target of 1.5 million ZEVs (a mix of PHEVs, BEVs and FCEVs) on the road by 2025, on a path to 5 million by 2030. California has established mandates that require auto manufacturers to make available specific numbers of these vehicles to support achieving these targets. Reference

So, with intervention of the state policy, the transition from heavily reply on fossil fuel to EV is rather successful in the CA state comparing to other states.